Merge pull request #5203 from satijalab/develop

Seurat v4.0.5

.github/workflows/R_CMD_check.yaml

@@ -22,7 +22,7 @@ jobs:
               shell: bash
 
             - name: Check
-              run:  devtools::check(args = "--no-manual", error_on = "warning", check_dir = "check")
+              run:  devtools::check(args = "--no-manual", error_on = "warning", check_dir = "check", force_suggests = FALSE)
               shell: Rscript {0}
 
             - name: Upload check results

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: Seurat
-Version: 4.0.4
-Date: 2021-08-19
+Version: 4.0.5
+Date: 2021-10-04
 Title: Tools for Single Cell Genomics
 Description: A toolkit for quality control, analysis, and exploration of single cell RNA sequencing data. 'Seurat' aims to enable users to identify and interpret sources of heterogeneity from single cell transcriptomic measurements, and to integrate diverse types of single cell data. See Satija R, Farrell J, Gennert D, et al (2015) <doi:10.1038/nbt.3192>, Macosko E, Basu A, Satija R, et al (2015) <doi:10.1016/j.cell.2015.05.002>, Stuart T, Butler A, et al (2019) <doi:10.1016/j.cell.2019.05.031>, and Hao, Hao, et al (2020) <doi:10.1101/2020.10.12.335331> for more details.
 Authors@R: c(

NEWS.md

@@ -1,3 +1,9 @@
+## Seurat 4.0.5 (2020-10-04)
+## Changes
+- Update documentation for `to.upper` parameter in `Load10X_Spatial()` ([#4576](https://github.com/satijalab/seurat/issues/4576))
+- Update concept tags for `RunSPCA()` ([#4978](https://github.com/satijalab/seurat/discussions/4987))
+- Conditionally run tests/packages that use suggested packages ([#5160](https://github.com/satijalab/seurat/pull/5160))
+
 ## Seurat 4.0.4 (2020-08-19)
 ## Added
 - Add `reduction` parameter to `BuildClusterTree()` ([#4598](https://github.com/satijalab/seurat/issues/4598))
@@ -7,6 +13,7 @@
 - Add `densify` parameter to `FindMarkers()`
 - Add `ReadParsebio()` function to read output from Parse Biosciences
 - Add the `image.alpha` parameter to `SpatialDimPlot()` and `SpatialFeaturePlot()`
+- Add support for the correlation metric in `RunUMAP` ([#4972](https://github.com/satijalab/seurat/issues/4972))
 
 ## Changes
 - Warn and continue rather than erroring if not all features are available in `FindSpatiallyVariableFeatures()` ([#4611](https://github.com/satijalab/seurat/issues/4611))

R/data.R

@@ -8,7 +8,7 @@
 #'   \item{g2m.genes}{Genes associated with G2M-phase}
 #' }
 #' @concept data
-#' @source \url{https://science.sciencemag.org/content/352/6282/189}
+#' @source \url{https://www.science.org/doi/abs/10.1126/science.aad0501}
 #'
 "cc.genes"
 
@@ -41,7 +41,7 @@
 #'   \item{g2m.genes}{Genes associated with G2M-phase}
 #' }
 #' @concept data
-#' @source \url{https://science.sciencemag.org/content/352/6282/189}
+#' @source \url{https://www.science.org/doi/abs/10.1126/science.aad0501}
 #'
 #' @seealso \code{\link{cc.genes}}
 #'

R/dimensional_reduction.R

@@ -1309,14 +1309,6 @@ RunUMAP.default <- function(
       umap$fit_transform(as.matrix(x = object))
     },
     'uwot' = {
-      if (metric == 'correlation') {
-        warning(
-          "UWOT does not implement the correlation metric, using cosine instead",
-          call. = FALSE,
-          immediate. = TRUE
-        )
-        metric <- 'cosine'
-      }
       if (is.list(x = object)) {
         umap(
           X = NULL,
@@ -2311,6 +2303,7 @@ PrepDR <- function(
 #'
 #' @importFrom irlba irlba
 #'
+#' @concept dimensional_reduction
 #' @rdname RunSPCA
 #' @export
 RunSPCA.default <- function(
@@ -2352,6 +2345,7 @@ RunSPCA.default <- function(
 #' using the variable features for the Assay.
 #'
 #' @rdname RunSPCA
+#' @concept dimensional_reduction
 #' @export
 #' @method RunSPCA Assay
 #'
@@ -2386,6 +2380,7 @@ RunSPCA.Assay <- function(
 
 #' @param reduction.name dimensional reduction name, spca by default
 #' @rdname RunSPCA
+#' @concept dimensional_reduction
 #' @export
 #' @method RunSPCA Seurat
 #'

R/generics.R

@@ -128,9 +128,11 @@ FindClusters <- function(object, ...) {
 #'
 #' # Pass 'clustertree' or an object of class phylo to ident.1 and
 #' # a node to ident.2 as a replacement for FindMarkersNode
-#' pbmc_small <- BuildClusterTree(object = pbmc_small)
-#' markers <- FindMarkers(object = pbmc_small, ident.1 = 'clustertree', ident.2 = 5)
-#' head(x = markers)
+#' if (requireNamespace("ape", quietly = TRUE)) {
+#'   pbmc_small <- BuildClusterTree(object = pbmc_small)
+#'   markers <- FindMarkers(object = pbmc_small, ident.1 = 'clustertree', ident.2 = 5)
+#'   head(x = markers)
+#' }
 #'
 #' @rdname FindMarkers
 #' @export FindMarkers

R/integration.R

@@ -1271,8 +1271,23 @@ GetTransferPredictions <- function(object, assay = "predictions", slot = "data",
 #' query, as the merged object will subsequently contain more cells than was in
 #' query, and weights will need to be calculated for all cells in the object.
 #' @param sd.weight Controls the bandwidth of the Gaussian kernel for weighting
-#' @param sample.tree Specify the order of integration. If NULL, will compute
-#' automatically.
+#' @param sample.tree Specify the order of integration. Order of integration
+#' should be encoded in a matrix, where each row represents one of the pairwise
+#' integration steps. Negative numbers specify a dataset, positive numbers
+#' specify the integration results from a given row (the format of the merge
+#' matrix included in the \code{\link{hclust}} function output). For example:
+#' `matrix(c(-2, 1, -3, -1), ncol = 2)` gives:
+#'
+#' ```
+#'             [,1]  [,2]
+#'        [1,]   -2   -3
+#'        [2,]    1   -1
+#' ```
+#'
+#' Which would cause dataset 2 and 3 to be integrated first, then the resulting
+#' object integrated with dataset 1.
+#'
+#'  If NULL, the sample tree will be computed automatically.
 #' @param preserve.order Do not reorder objects based on size for each pairwise
 #' integration.
 #' @param eps Error bound on the neighbor finding algorithm (from
@@ -1291,6 +1306,7 @@ GetTransferPredictions <- function(object, assay = "predictions", slot = "data",
 #'
 #' @export
 #' @concept integration
+#' @md
 #' @examples
 #' \dontrun{
 #' # to install the SeuratData package see https://github.com/satijalab/seurat-data

R/preprocessing.R

@@ -468,8 +468,10 @@ GetResidual <- function(
 #' @param slice Name for the stored image of the tissue slice
 #' @param filter.matrix Only keep spots that have been determined to be over
 #' tissue
-#' @param to.upper Converts all feature names to upper case. Can be useful when
-#' analyses require comparisons between human and mouse gene names for example.
+#' @param to.upper Converts all feature names to upper case. This can provide an
+#' approximate conversion of mouse to human gene names which can be useful in an
+#' explorative analysis. For cross-species comparisons, orthologous genes should
+#' be identified across species and used instead.
 #' @param image An object of class VisiumV1. Typically, an output from \code{\link{Read10X_Image}}
 #' @param ... Arguments passed to \code{\link{Read10X_h5}}
 #'

R/tree.R

@@ -49,10 +49,12 @@ cluster.ape <- paste(
 #' @concept tree
 #'
 #' @examples
-#' data("pbmc_small")
-#' pbmc_small
-#' pbmc_small <- BuildClusterTree(object = pbmc_small)
-#' Tool(object = pbmc_small, slot = 'BuildClusterTree')
+#' if (requireNamespace("ape", quietly = TRUE)) {
+#'   data("pbmc_small")
+#'   pbmc_small
+#'   pbmc_small <- BuildClusterTree(object = pbmc_small)
+#'   Tool(object = pbmc_small, slot = 'BuildClusterTree')
+#' }
 #'
 BuildClusterTree <- function(
   object,

R/visualization.R

@@ -651,11 +651,12 @@ VlnPlot <- function(
 #' @seealso \code{\link{DimPlot}}
 #'
 #' @examples
-#' data("pbmc_small")
-#' pbmc_small
-#' pbmc_small <- BuildClusterTree(object = pbmc_small, verbose = FALSE)
-#' PlotClusterTree(pbmc_small)
-#' ColorDimSplit(pbmc_small, node = 5)
+#' if (requireNamespace("ape", quietly = TRUE)) {
+#'   data("pbmc_small")
+#'   pbmc_small <- BuildClusterTree(object = pbmc_small, verbose = FALSE)
+#'   PlotClusterTree(pbmc_small)
+#'   ColorDimSplit(pbmc_small, node = 5)
+#' }
 #'
 ColorDimSplit <- function(
   object,
@@ -3823,10 +3824,11 @@ JackStrawPlot <- function(
 #' @concept visualization
 #'
 #' @examples
-#' data("pbmc_small")
-#' pbmc_small <- BuildClusterTree(object = pbmc_small)
-#' PlotClusterTree(object = pbmc_small)
-#'
+#' if (requireNamespace("ape", quietly = TRUE)) {
+#'   data("pbmc_small")
+#'   pbmc_small <- BuildClusterTree(object = pbmc_small)
+#'   PlotClusterTree(object = pbmc_small)
+#' }
 PlotClusterTree <- function(object, direction = "downwards", ...) {
   if (!PackageCheck('ape', error = FALSE)) {
     stop(cluster.ape, call. = FALSE)

README.md

@@ -1,4 +1,4 @@
-[![Build Status](https://travis-ci.com/satijalab/seurat.svg?branch=master)](https://travis-ci.com/github/satijalab/seurat)
+[![Build Status](https://travis-ci.com/satijalab/seurat.svg?branch=master)](https://app.travis-ci.com:443/github/satijalab/seurat)
 [![AppVeyor build status](https://ci.appveyor.com/api/projects/status/github/satijalab/seurat?branch=master&svg=true)](https://ci.appveyor.com/project/satijalab/seurat)
 [![CRAN Version](https://www.r-pkg.org/badges/version/Seurat)](https://cran.r-project.org/package=Seurat)
 [![CRAN Downloads](https://cranlogs.r-pkg.org/badges/Seurat)](https://cran.r-project.org/package=Seurat)

cran-comments.md

@@ -1,11 +1,11 @@
-# Seurat v4.0.4
+# Seurat v4.0.5
 
 ## Test environments
 * local Ubuntu 20.04 install, R 4.1.0
 * Ubuntu 16.04.6 (on travis-ci), R 4.0.0, R devel
 * macOS 10.13.6 (on travis-ci), R 4.0.2
 * Windows Server 2012 R2 (on AppVeyor), R 4.1.0 Patched
-* win-builder (oldrelease, release, devel)
+* win-builder (release, devel)
 
 ## R CMD check results
 There were no ERRORs, WARNINGs, or NOTEs
@@ -14,6 +14,6 @@ There were no ERRORs, WARNINGs, or NOTEs
 
 There is one package that depends on Seurat: tidyseurat; this update does not impact its functionality
 
-There are eight packages that import Seurat: CDSeq, DUBStepR, PhitestR, rPanglaoDB, scMappR, Signac, SignacX, and SoupX; this update does not impact their functionality
+There are ten packages that import Seurat: CDSeq, DUBStepR, PhitestR, rPanglaoDB, scDiffCom, scMappR, scRNAstat, Signac, SignacX, and SoupX; this update does not impact their functionality
 
 There are ten packages that suggest Seurat: BisqueRNA, ClustAssess, clustree, conos, DIscBIO, dyngen, harmony, rliger, Rmagic, and VAM; this update does not impact their functionality.